EMBO/soda-vec-data-full_pmc_title_abstract

SODA-VEC Clean Dataset

This is a cleaned and filtered version of the SODA-VEC dataset, containing high-quality biomedical title-abstract pairs from PubMed Central (PMC) articles.

Dataset Overview

• Total examples: 26,573,900
• Training set: 26,473,900 examples (99.6%)
• Validation set: 50,000 examples (0.2%)
• Test set: 50,000 examples (0.2%)

Quality Filtering Applied

This dataset has been processed with the following quality filters:

Abstract Length Filtering

• Minimum length: 128 characters
• Maximum length: 6,000 characters
• Rationale: Removes fragments and overly long texts while preserving scientific abstracts

Retention Statistics

• Original dataset: ~26.6M examples
• After filtering: 26,573,900 examples
• Retention rate: ~99.7%

Content Statistics (sample):

• Title length: ~100 ± 50 chars
• Abstract length: ~1,300 ± 600 chars
• Title range: 10-500 chars
• Abstract range: 128-6,000 chars

Length Distributions

The dataset shows well-balanced length distributions:

• Title lengths: Centered around 100 characters with good variance
• Abstract lengths: Normally distributed around 1,300 characters
• Quality filtering: Clearly removes outliers while preserving natural variation

Data Fields

Each example contains:

• title (string): The title of the scientific article
• abstract (string): The abstract of the scientific article
• pmcid (string): PubMed Central ID for the article

Intended Use

This dataset is designed for:

Primary Applications

• Scientific text embeddings: Training domain-specific embedding models
• Biomedical NLP: Fine-tuning language models on scientific literature
• Semantic similarity: Learning representations for scientific text matching
• Information retrieval: Building search systems for biomedical literature

Research Applications

• Representation learning for scientific texts
• Cross-modal learning (title-abstract relationships)
• Domain adaptation for biomedical language models
• Evaluation of scientific text understanding systems

Data Source & Methodology

Original Dataset

Based on the SODA-VEC dataset from PubMed Central articles.

Processing Pipeline

1. Data loading: Combined train/validation/test splits from original dataset
2. Quality filtering: Applied length-based filters to ensure high-quality pairs
3. Split creation: Created new balanced train/validation/test splits
4. Validation: Verified data integrity and distribution balance

Quality Assurance

• Length distribution analysis
• Duplicate detection and removal
• Content quality validation
• Statistical validation of splits

Usage Example

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("EMBO/soda-vec-data-full_pmc_title_abstract")

# Access different splits
train_data = dataset["train"]
val_data = dataset["validation"] 
test_data = dataset["test"]

# Example usage
for example in train_data.take(1):
    print(f"Title: {example['title']}")
    print(f"Abstract: {example['abstract']}")
    print(f"PMC ID: {example['pmcid']}")

Citation

If you use this dataset in your research, please cite the original SODA-VEC paper:

@article{soda-vec-2024,
  title={SODA-VEC: Training Vector Representations of Scientific Literature},
  author={...},
  journal={...},
  year={2024}
}

License

This dataset is released under the CC-BY-4.0 license, consistent with PubMed Central's open access requirements.

Contact

For questions about this dataset, please contact the EMBO team or open an issue in the dataset repository.

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Dataset processed with quality filters and balanced splits for optimal training performance.